Python For Data Analysis and Data Science: Zero To Mastery With Pandas

A Practical Approach To Learn Pandas From Basic To Advanced Level With 100 + Exercises and Real Life Projects

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Last Updated: 2022-04-20 11:04:22 English Course Type: Self Learning

Build a Solid Foundation in Data Analysis with Python
Manipulate data quickly and efficiently
How to code with Pandas toolkit
Able to analyze a large file
Learn hundreds of methods and attributes across numerous pandas objects
Create dataframes with Pandas and recognize analytical approaches to data

Hello, dear learning aspirants welcome to “Python For Data Analysis and Data Science: Zero To Mastery With Pandas ”. We love programming. Python is one of the most popular programming languages in today is technical world. Python offers both object oriented and structural programming features. Hence, we are interested in data analysis with Pandas in this course.

This course is for those who are ready to take their data analysis skill to the next higher level with Python data analysis toolkit, i.e. "Pandas". This tutorial is designed for beginners and intermediates but that does not mean that we will not talk about the advanced stuff as well. Our approach of teaching in this tutorial is simple and straightforward, no complications are included to make bored Or lose concentration.

In this tutorial, I will be covering all the basic things you wil need to know about the Pandas to become a data analyst or data scientist. We are adopting a hands on approach to learn things easily and comfortably. You will enjoy learning as well as the exercises to practice along with the real life projects (The projects included are the part of large size research oriented industry projects).

I trust and believe that, it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasists.

I wish you all good luck with the course and happy learning. Let's dive into the course and engage with the knowledge.

To your Success

Pruthviraja L

No Prior Knowledge or Experience Needed, Only a Passion to Learn !
Download Anaconda 4.2.0, the free data science platform by Continuum, which contains Python 3.5.2 and pandas 0.19.2.
Basic knowledge of data types (Basic Maths, integers, floating point numbers, Logic Conditions) etc, but not necessary
Basic Or intermediate experience with Microsoft Excel, but not necessary.

Data Analysis Beginner
Business and Analyst
Students and Other Professionals
Beginner Python developers Curious to learn about Data Science
Aspiring data scientists who want to add Python to their tool arsenal

Section 1 : Getting Started 7 Lectures 00:50:51
Lecture 1 :
Course Introduction Preview
Lecture 2 :
How To Get Most Out Of This Course
Lecture 3 :
Better To Know These Things
Lecture 4 :
How To Install Python IPython And Jupyter Notebook
Lecture 5 :
How To Install Anaconda For macOS And Linux Users
Lecture 6 :
How To Work With The Jupyter Notebook Part - 1
Lecture 7 :
How To Work With The Jupyter Notebook Part - 2
Section 2 : Pandas Building Blacks 2 Lectures 00:19:10
Lecture 1 :
How To Work With The Tabular Data
Lecture 2 :
How To Read The Documentation In Pandas
Section 3 : Pandas Data Structures 6 Lectures 01:09:25
Lecture 1 :
Theory On Pandas Data Structures
Lecture 2 :
How To Construct The Pandas Series
Lecture 3 :
How To Construct The DataFrame Objects
Lecture 4 :
How Construct The Pandas Index Objects
Lecture 5 :
Practice_Part_01
Lecture 6 :
Practice_Part_01 Solution
Section 4 : Data Indexing and Selection In Pandas 9 Lectures 00:58:23
Lecture 1 :
Theory On Data Indexing And Selection
Lecture 2 :
Data Selection In Series Part 1
Lecture 3 :
Data Selection In Series Part 2
Lecture 4 :
Indexers Loc And Iloc In Series
Lecture 5 :
Data Selection In DataFrame Part 1
Lecture 6 :
Data Selection In DataFrame Part 2
Lecture 7 :
Accessing Values Using Loc Iloc And Ix In DataFrame Objects
Lecture 8 :
Practice_Part_02
Lecture 9 :
Practice_Part_02 Solution
Section 5 : Essential Functionalities 13 Lectures 02:03:12
Lecture 1 :
Theory On Essential Functionalities
Lecture 2 :
How To Reindex Pandas Objects
Lecture 3 :
How To Drop Along Rows and Columns Axis
Lecture 4 :
Arithmetic And Data Alignment
Lecture 5 :
Arithmetic Methods With Fill Values
Lecture 6 :
Broadcasting In Pandas
Lecture 7 :
Apply And Applymap In Pandas
Lecture 8 :
How To Sort And Rank In Pandas
Lecture 9 :
How To Work With The Duplicated Indices
Lecture 10 :
Summarising And Computing Descriptive Statistics
Lecture 11 :
Unique Values Value Counts And Membership
Lecture 12 :
Practice_Part_03
Lecture 13 :
Practice_Part_03 Solution
Section 6 : Data Handling 8 Lectures 01:24:38
Lecture 1 :
Theory On Data Handling
Lecture 2 :
How To Read The Csv Files Part - 1
Lecture 3 :
How To Read The Csv Files Part - 2
Lecture 4 :
How To Read Text Files In Pieces
Lecture 5 :
How To Export Data In Text Format
Lecture 6 :
How To Use Python's Csv Module
Lecture 7 :
Practice_Part_04
Lecture 8 :
Practice_Part_04 Solution
Section 7 : Data Cleaning and Preparation 17 Lectures 02:54:13
Lecture 1 :
Theory On Data Preprocessing
Lecture 2 :
How To Handle Missing Values
Lecture 3 :
How To Filter The Missing Values
Lecture 4 :
How To Filter The Missing Values Part 2
Lecture 5 :
How To Remove Duplicate Rows And Values
Lecture 6 :
How To Replace The Non Null Values
Lecture 7 :
How To Rename The Axis Labels
Lecture 8 :
How To Discretize And Bin The Data
Lecture 9 :
How To Filter And Detect The Outliers
Lecture 10 :
How To Reorder And Select Randomly
Lecture 11 :
Converting The Categorical Variables Into Dummy Variables
Lecture 12 :
How To Use 'map' Method
Lecture 13 :
How To Manipulate With Strings
Lecture 14 :
Using Regular Expressions
Lecture 15 :
Working With The Vectorized String Functions
Lecture 16 :
Practice_Part_05
Lecture 17 :
Practice_Part_05 Solution
Section 8 : Data Wrangling 12 Lectures 01:45:08
Lecture 1 :
Theory On Data Wrangling
Lecture 2 :
Hierarchical Indexing
Lecture 3 :
Hierarchical Indexing Reordering And Sorting
Lecture 4 :
Summary Statistics By Level
Lecture 5 :
Hierarchical Indexing With DataFrame Columns
Lecture 6 :
How To Merge The Pandas Objects
Lecture 7 :
Merging On Row Index
Lecture 8 :
How To Concatenate Along An Axis
Lecture 9 :
How To Combine With Overlap
Lecture 10 :
How To Reshape And Pivot Data In Pandas
Lecture 11 :
Practice_Part_06
Lecture 12 :
Practice_Part_06 Solution
Section 9 : Data Grouping and Aggregation 9 Lectures 01:03:08
Lecture 1 :
Theory On Data Groupby And Aggregation
Lecture 2 :
Groupby Operation
Lecture 3 :
How To Iterate Over Groupby Object
Lecture 4 :
How To Select Columns In Groupby Method
Lecture 5 :
Grouping Using Dictionaries And Series
Lecture 6 :
Grouping Using Functions And Index Level
Lecture 7 :
Data Aggregation
Lecture 8 :
Practice_Part_07
Lecture 9 :
Practice_Part_07 Solution
Section 10 : Time Series Analysis 10 Lectures 01:40:44
Lecture 1 :
Theory On Time Series Analysis
Lecture 2 :
Introduction To Time Series Data Types
Lecture 3 :
How To Convert Between String And Datetime
Lecture 4 :
Time Series Basics With Pandas Objects
Lecture 5 :
Date Ranges, Frequencies And Shifting Part - 1
Lecture 6 :
Date Ranges Frequencies And Shifting Part - 2
Lecture 7 :
Time Zone Handling
Lecture 8 :
Periods And Period Arithmeticâ€™s
Lecture 9 :
Practice_Part_08
Lecture 10 :
Practice_Part_08 Solution
Section 11 : Analysing Real Life Projects Using Open Source Data 10 Lectures 01:35:24
Lecture 1 :
A Brief Introduction To The Pandas Projects
Lecture 2 :
Project_1 Description
Lecture 3 :
Project_1 Solution Part - 1
Lecture 4 :
Project_1 Solution Part - 2
Lecture 5 :
Project_2 Description
Lecture 6 :
Project_2 Solution
Lecture 7 :
Project_3 Description
Lecture 8 :
Project_3 Solution Part - 1
Lecture 9 :
Project_3 Solution Part - 2
Lecture 10 :
Project Assignment

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Pruthviraja L,

296201 Course Views

3 Courses

Hi, I am Pruthivraja L, with more than 7 plus years of teaching and training experience from various technical institutes. Teaching is my passion. I'm a Certified Data Analyst. I got certifications from various eLearning centers including Udemy, Intellipaat-Bengaluru, LinkedIn eLearning center, etc. My teaching skills are Matlab, Python, SAS, R, AI and Machine learning, Data Science and Data Analysis. I love teaching and training because I knew sharing knowledge with the globe through digital media makes us feel proud and we also get satisfaction for empowering and enlightening the new learning aspirants & enthusiasts of different skills. I'm a multi-faceted software professional aspirant with demonstrated capability in deploying analytical and programming methodologies to extract insights for boosting and bolstering user requirements. Adept at conducting statistical analysis and data modeling for transforming raw data into actionable strategies. I've written a student-friendly textbook in the electrical engineering field titled 'Elements of Electrical Engineering' under the publication of 'I.K. International Publishing House Pvt Ltd', New Delhi, India. The book is available in many countries including the USA and UK via Amazon and many other seller portals. The book is now started distributing under Wiley India Pvt. Ltd.